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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/system_overview.html">System Overview</a></li>
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  <h1>Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">inspect</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span><span class="p">,</span> <span class="n">auto</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Iterator</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.export._trace</span><span class="w"> </span><span class="kn">import</span> <span class="n">_export</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._Device</span><span class="w"> </span><span class="kn">import</span> <span class="n">Device</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">dtype</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo</span><span class="w"> </span><span class="kn">import</span> <span class="n">_defaults</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">dynamo_compile</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._refit</span><span class="w"> </span><span class="kn">import</span> <span class="n">refit_module_weights</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">check_output_equal</span><span class="p">,</span>
    <span class="n">deallocate_module</span><span class="p">,</span>
    <span class="n">to_torch_device</span><span class="p">,</span>
    <span class="n">to_torch_tensorrt_device</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<span class="k">class</span><span class="w"> </span><span class="nc">RefitFlag</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
    <span class="n">UNKNOWN</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
    <span class="n">NEEDS_REFIT</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
    <span class="n">NEEDS_RECOMPILE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
    <span class="n">LIVE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>


<span class="k">class</span><span class="w"> </span><span class="nc">RefitState</span><span class="p">:</span>
    <span class="n">_state</span><span class="p">:</span> <span class="n">RefitFlag</span> <span class="o">=</span> <span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">set_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">RefitFlag</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">RefitFlag</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_state</span> <span class="o">=</span> <span class="n">state</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid state: </span><span class="si">{</span><span class="n">state</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">get_state</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RefitFlag</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_state</span>


<span class="k">class</span><span class="w"> </span><span class="nc">DynamicShapeOutOfRangeException</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
    <span class="k">pass</span>


<div class="viewcode-block" id="MutableTorchTensorRTModule"><a class="viewcode-back" href="../../../../py_api/torch_tensorrt.html#torch_tensorrt.MutableTorchTensorRTModule">[docs]</a><span class="k">class</span><span class="w"> </span><span class="nc">MutableTorchTensorRTModule</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Initialize a MutableTorchTensorRTModule to seamlessly manipulate it like a regular PyTorch module.</span>
<span class="sd">    All TensorRT compilation and refitting processes are handled automatically as you work with the module.</span>
<span class="sd">    Any changes to its attributes or loading a different state_dict will trigger refitting or recompilation,</span>
<span class="sd">    which will be managed during the next forward pass.</span>

<span class="sd">    The MutableTorchTensorRTModule takes a PyTorch module and a set of configuration settings for the compiler.</span>
<span class="sd">    Once compilation is complete, the module maintains the connection between the TensorRT graph module and the original PyTorch module.</span>
<span class="sd">    Any modifications made to the MutableTorchTensorRTModule will be reflected in both the TensorRT graph module and the original PyTorch module.</span>
<span class="sd">    &quot;&quot;&quot;</span>

<div class="viewcode-block" id="MutableTorchTensorRTModule.__init__"><a class="viewcode-back" href="../../../../py_api/torch_tensorrt.html#torch_tensorrt.MutableTorchTensorRTModule.__init__">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">pytorch_model</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
        <span class="o">*</span><span class="p">,</span>
        <span class="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DEVICE</span><span class="p">,</span>
        <span class="n">use_python_runtime</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
        <span class="n">immutable_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">strict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">prefer_deferred_runtime_asserts_over_guards</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">weight_streaming_budget</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
            <span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]</span>
        <span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span><span class="p">,</span>
        <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>

<span class="sd">        Arguments:</span>
<span class="sd">            pytorch_model (torch.nn.module): Source module that needs to be accelerated</span>

<span class="sd">        Keyword Arguments:</span>
<span class="sd">            device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>

<span class="sd">                device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>

<span class="sd">            disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
<span class="sd">            assume_dynamic_shape_support (bool): Setting this to true enables the converters work for both dynamic and static shapes. Default: False</span>
<span class="sd">            sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
<span class="sd">            enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">            immutable_weights (bool): Build non-refittable engines. This is useful for some layers that are not refittable.</span>
<span class="sd">            capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
<span class="sd">            num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
<span class="sd">            workspace_size (int): Maximum size of workspace given to TensorRT</span>
<span class="sd">            dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
<span class="sd">            dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
<span class="sd">            dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
<span class="sd">            truncate_double (bool): Truncate weights provided in double (float64) to float32</span>
<span class="sd">            require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
<span class="sd">            min_block_size (int): The minimum number of contiguous TensorRT convertible operations in order to run a set of operations in TensorRT</span>
<span class="sd">            torch_executed_ops (Collection[Target]): Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but ``require_full_compilation`` is True</span>
<span class="sd">            torch_executed_modules (List[str]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
<span class="sd">            pass_through_build_failures (bool): Error out if there are issues during compilation (only applicable to torch.compile workflows)</span>
<span class="sd">            max_aux_stream (Optional[int]): Maximum streams in the engine</span>
<span class="sd">            version_compatible (bool): Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</span>
<span class="sd">            optimization_level: (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</span>
<span class="sd">            use_python_runtime: (bool): Return a graph using a pure Python runtime, reduces options for serialization</span>
<span class="sd">            use_fast_partitioner: (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optimal. Use the global paritioner (``False``) if looking for best performance</span>
<span class="sd">            enable_experimental_decompositions (bool): Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</span>
<span class="sd">            dryrun (bool): Toggle for &quot;Dryrun&quot; mode, running everything except conversion to TRT and logging outputs</span>
<span class="sd">            hardware_compatible (bool): Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</span>
<span class="sd">            timing_cache_path (str): Path to the timing cache if it exists (or) where it will be saved after compilation</span>
<span class="sd">            lazy_engine_init (bool): Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.</span>
<span class="sd">            enabled_precisions (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">            **kwargs: Any,</span>
<span class="sd">        Returns:</span>
<span class="sd">            MutableTorchTensorRTModule</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># The order to initialize this module is</span>
        <span class="c1"># 1. Set init_finished to False</span>
        <span class="c1"># 2. Initialize all attributes</span>
        <span class="c1"># 3. Add the module base class</span>
        <span class="c1"># 4. Set the init_finished to True</span>
        <span class="c1"># After initialization, no new attribute should be added to the module __dict__</span>
        <span class="c1"># Otherwise, it will cause undefined behavior</span>

        <span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;init_finished&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span> <span class="o">=</span> <span class="n">RefitState</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pytorch_model</span> <span class="o">=</span> <span class="n">_make_refit_change_trigger</span><span class="p">(</span><span class="n">pytorch_model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span> <span class="o">=</span> <span class="n">pytorch_model</span>
        <span class="k">if</span> <span class="n">pytorch_model</span><span class="o">.</span><span class="n">training</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="s2">&quot;The model may be in training mode, which may affect the performance of the compiled model!&quot;</span>
            <span class="p">)</span>
        <span class="c1"># Process settings</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">gm</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">additional_settings</span> <span class="o">=</span> <span class="n">kwargs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">strict</span> <span class="o">=</span> <span class="n">strict</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">prefer_deferred_runtime_asserts_over_guards</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">prefer_deferred_runtime_asserts_over_guards</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_python_runtime</span> <span class="o">=</span> <span class="n">use_python_runtime</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">trt_device</span> <span class="o">=</span> <span class="n">to_torch_tensorrt_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
        <span class="k">assert</span> <span class="p">(</span>
            <span class="ow">not</span> <span class="n">immutable_weights</span>
        <span class="p">),</span> <span class="s2">&quot;`immutable_weights has to be False for a MutableTorchTensorRTModule&quot;</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">serializable_dynamic_shapes_dims</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">run_info</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">state_dict_metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_store_state_dict_metadata</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">enable_weight_streaming</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;enable_weight_streaming&quot;</span><span class="p">]</span>
            <span class="k">if</span> <span class="s2">&quot;enable_weight_streaming&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span>
            <span class="k">else</span> <span class="kc">False</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_ctx</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_budget</span> <span class="o">=</span> <span class="n">weight_streaming_budget</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">enable_weight_streaming</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">weight_streaming_budget</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="s2">&quot;Weight stremaing budget is not set. Using auto weight streaming budget&quot;</span>
                <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">enabled_precisions</span> <span class="o">=</span> <span class="n">enabled_precisions</span>

        <span class="bp">cls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span>
        <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
            <span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">pytorch_model</span><span class="o">.</span><span class="vm">__class__</span><span class="p">),</span>
            <span class="p">{},</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">init_finished</span> <span class="o">=</span> <span class="kc">True</span></div>

<div class="viewcode-block" id="MutableTorchTensorRTModule.set_expected_dynamic_shape_range"><a class="viewcode-back" href="../../../../py_api/torch_tensorrt.html#torch_tensorrt.MutableTorchTensorRTModule.set_expected_dynamic_shape_range">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">set_expected_dynamic_shape_range</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">args_dynamic_shape</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]],</span>
        <span class="n">kwargs_dynamic_shape</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Set the dynamic shape range. The shape hint should EXACTLY follow arg_inputs and kwarg_inputs passed to the forward function</span>
<span class="sd">        and should not omit any entries (except None in the kwarg_inputs). If there is a nested dict/list in the input, the dynamic shape for that entry should also be an nested dict/list.</span>
<span class="sd">        If the dynamic shape is not required for an input, an empty dictionary should be given as the shape hint for that input.</span>
<span class="sd">        Note that you should exclude keyword arguments with value None as those will be filtered out.</span>

<span class="sd">        Example:</span>
<span class="sd">        def forward(a, b, c=0, d=0):</span>
<span class="sd">            pass</span>

<span class="sd">        seq_len = torch.export.Dim(&quot;seq_len&quot;, min=1, max=10)</span>
<span class="sd">        args_dynamic_shape = ({0: seq_len}, {}) # b does not have a dynamic shape</span>
<span class="sd">        kwargs_dynamic_shape = {&#39;c&#39;: {0, seq_len}, &#39;d&#39;: {}} # d does not have a dynamic shape</span>
<span class="sd">        set_expected_dynamic_shape_range(args_dynamic_shape, kwargs_dynamic_shape)</span>
<span class="sd">        # Later when you call the function</span>
<span class="sd">        forward(*(a, b), **{c:..., d:...})</span>

<span class="sd">        Reference: https://pytorch.org/docs/stable/export.html#expressing-dynamism</span>
<span class="sd">        Arguments:</span>
<span class="sd">            args_dynamic_shape (tuple[dict[Any, Any]]): Dynamic shape hint for the arg_inputs,</span>
<span class="sd">            kwargs_dynamic_shape: (dict[str, Any]): Dynamic shape hint for the kwarg_inputs</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span>
            <span class="n">args_dynamic_shape</span><span class="p">,</span> <span class="nb">tuple</span>
        <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;args dynamic shape has to be a tuple, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">args_dynamic_shape</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span>
            <span class="n">kwargs_dynamic_shape</span><span class="p">,</span> <span class="nb">dict</span>
        <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;args dynamic shape has to be a dictionary, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs_dynamic_shape</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span> <span class="o">=</span> <span class="n">kwargs_dynamic_shape</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span> <span class="o">=</span> <span class="n">args_dynamic_shape</span>

        <span class="c1"># Clear cached inputs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">)</span></div>

    <span class="k">def</span><span class="w"> </span><span class="nf">_get_total_dynamic_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="kc">None</span><span class="p">]:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">None</span>
        <span class="n">total_dynamic_shape</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">:</span>
            <span class="n">signature</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
                <span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">forward</span><span class="p">)</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
            <span class="p">)</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">arg</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">):</span>
                <span class="n">total_dynamic_shape</span><span class="p">[</span><span class="n">signature</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">arg</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">kwargs_dynamic_shape</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="n">total_dynamic_shape</span><span class="p">[</span><span class="n">kwargs</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs_dynamic_shape</span>

        <span class="k">return</span> <span class="n">total_dynamic_shape</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_store_state_dict_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">state_dict_metadata</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">load_state_dict</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">strict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">assign</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_REFIT</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="n">strict</span><span class="p">,</span> <span class="n">assign</span><span class="o">=</span><span class="n">assign</span><span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">_transform_state_dict</span><span class="p">(</span><span class="n">sd</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">]:</span>
        <span class="k">return</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">sd</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">update_refit_condition</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># 2-stage check to determine whether the module should be intact, refitted, or recompiled.</span>

        <span class="c1"># Default refit</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_REFIT</span><span class="p">)</span>

        <span class="c1"># Run the same inputs through pytorch model and compare the result to previous run of graph module</span>
        <span class="c1"># to determine whether refit/recompilation is needed. If the output is the same, no further process needed.</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_info</span><span class="p">:</span>
            <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_info</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">to_torch_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trt_device</span><span class="p">))</span>
            <span class="n">new_result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
            <span class="n">deallocate_module</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">check_output_equal</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">new_result</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">LIVE</span><span class="p">)</span>
                <span class="k">return</span>

        <span class="c1"># Since we do not have access to the previous state_dict, we can only use state_dict_metadata</span>
        <span class="c1"># to determine whether the keys or weight shape is changed.</span>
        <span class="n">sd</span><span class="p">,</span> <span class="n">sd_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_dict_metadata</span>
        <span class="k">if</span> <span class="n">sd</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="o">!=</span> <span class="n">sd_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
            <span class="c1"># If keys are not identical, recompile.</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">)</span>
            <span class="k">return</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sd</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
            <span class="k">if</span> <span class="n">sd</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">sd_meta</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
                <span class="c1"># If weight shapes are not identical, recompile.</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">)</span>
                <span class="k">return</span>

        <span class="k">return</span>

<div class="viewcode-block" id="MutableTorchTensorRTModule.refit_gm"><a class="viewcode-back" href="../../../../py_api/torch_tensorrt.html#torch_tensorrt.MutableTorchTensorRTModule.refit_gm">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">refit_gm</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Refit the TRT graph module with any updates.</span>
<span class="sd">        This function should be called whenever the weight values get changed but the weight structure remains</span>
<span class="sd">        the same.</span>
<span class="sd">        MutableTorchTensorRTModule automatically catches weight value updates and call this function to refit the module.</span>
<span class="sd">        If it fails to catch the changes, please call this function manually to update the TRT graph module.</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">to_torch_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trt_device</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_exported_program</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span><span class="o">.</span><span class="n">_state_dict</span> <span class="o">=</span> <span class="p">(</span>
                <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_transform_state_dict</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
                <span class="p">)</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span><span class="o">.</span><span class="n">module</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">to_torch_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trt_device</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">gm</span> <span class="o">=</span> <span class="n">refit_module_weights</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">gm</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">,</span>
            <span class="n">use_weight_map_cache</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="n">in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="n">deallocate_module</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>

    <span class="k">def</span><span class="w"> </span><span class="nf">get_exported_program</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">ExportedProgram</span><span class="p">:</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">export_fn</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">ExportedProgram</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prefer_deferred_runtime_asserts_over_guards</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_export</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">,</span>
                    <span class="n">kwargs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">,</span>
                    <span class="n">dynamic_shapes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_total_dynamic_shapes</span><span class="p">(),</span>
                    <span class="n">strict</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">strict</span><span class="p">,</span>
                    <span class="n">prefer_deferred_runtime_asserts_over_guards</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prefer_deferred_runtime_asserts_over_guards</span><span class="p">,</span>
                <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">,</span>
                    <span class="n">kwargs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">,</span>
                    <span class="n">dynamic_shapes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_total_dynamic_shapes</span><span class="p">(),</span>
                    <span class="n">strict</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">strict</span><span class="p">,</span>
                <span class="p">)</span>

        <span class="c1"># Check if any quantization precision is enabled</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">enabled_precisions</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span>
            <span class="n">precision</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">enabled_precisions</span>
            <span class="k">for</span> <span class="n">precision</span> <span class="ow">in</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float8_e4m3fn</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float4_e2m1fn_x2</span><span class="p">)</span>
        <span class="p">):</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="kn">from</span><span class="w"> </span><span class="nn">modelopt.torch.quantization.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">export_torch_mode</span>

                <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">quantize_op</span><span class="o">.</span><span class="n">default</span>
            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="s2">&quot;Unable to import quantization op. Please install modelopt library (https://github.com/NVIDIA/TensorRT-Model-Optimizer?tab=readme-ov-file#installation) to add support for compiling quantized models&quot;</span>
                <span class="p">)</span>
            <span class="k">with</span> <span class="n">export_torch_mode</span><span class="p">():</span>
                <span class="k">return</span> <span class="n">export_fn</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">export_fn</span><span class="p">()</span>

<div class="viewcode-block" id="MutableTorchTensorRTModule.compile"><a class="viewcode-back" href="../../../../py_api/torch_tensorrt.html#torch_tensorrt.MutableTorchTensorRTModule.compile">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">compile</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        (Re)compile the TRT graph module using the PyTorch module.</span>
<span class="sd">        This function should be called whenever the weight structure get changed (shape, more layers...)</span>
<span class="sd">        MutableTorchTensorRTModule automatically catches weight value updates and call this function to recompile.</span>
<span class="sd">        If it fails to catch the changes, please call this function manually to recompile the TRT graph module.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># Export the module</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">to_torch_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trt_device</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_exported_program</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">gm</span> <span class="o">=</span> <span class="n">dynamo_compile</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">exp_program</span><span class="p">,</span>
            <span class="n">arg_inputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">,</span>
            <span class="n">kwarg_inputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">,</span>
            <span class="n">immutable_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="n">use_python_runtime</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_python_runtime</span><span class="p">,</span>
            <span class="n">enabled_precisions</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">enabled_precisions</span><span class="p">,</span>
            <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">additional_settings</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">additional_settings</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;offload_module_to_cpu&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span>
            <span class="n">deallocate_module</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">enable_weight_streaming</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">set_weight_streaming_ctx</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_budget</span><span class="p">)</span></div>

<div class="viewcode-block" id="MutableTorchTensorRTModule.set_weight_streaming_ctx"><a class="viewcode-back" href="../../../../py_api/torch_tensorrt.html#torch_tensorrt.MutableTorchTensorRTModule.set_weight_streaming_ctx">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">set_weight_streaming_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">requested_budget</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Set the weight streaming budget. If budget is not set, then automatic weight streaming budget</span>
<span class="sd">        is used.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_ctx</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">weight_streaming</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gm</span><span class="p">)</span>
        <span class="n">requested_budget</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">requested_budget</span>
            <span class="k">if</span> <span class="n">requested_budget</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
            <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_ctx</span><span class="o">.</span><span class="n">get_automatic_weight_streaming_budget</span><span class="p">()</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_ctx</span><span class="o">.</span><span class="n">device_budget</span> <span class="o">=</span> <span class="n">requested_budget</span></div>

    <span class="k">def</span><span class="w"> </span><span class="nf">_validate_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;First time compilation initiated. This may take some time.&quot;</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_store_inputs</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validates_dynamic_hints</span><span class="p">():</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                        <span class="s2">&quot;Invalid dynamic shape hint. Compiling module for the provided input shapes (static)&quot;</span>
                    <span class="p">)</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span> <span class="o">=</span> <span class="kc">None</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span> <span class="o">=</span> <span class="kc">None</span>
            <span class="k">return</span>

        <span class="c1"># If input does not equal or does not fall into dynamic shape range, recompile the engine</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_check_inputs_shape</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span>
            <span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_check_inputs_shape</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span>
            <span class="p">):</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Input change detected.&quot;</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_store_inputs</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
        <span class="k">except</span> <span class="n">DynamicShapeOutOfRangeException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Input change detected.&quot;</span><span class="p">)</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="s2">&quot;Provided inputs are outside the set expected shape range, recompiling module for the provided input shapes (static)&quot;</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span> <span class="o">=</span> <span class="kc">None</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span> <span class="o">=</span> <span class="kc">None</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_store_inputs</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_validates_dynamic_hints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;arg_dynamic_shape is not provided!&quot;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">):</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Warning: The length of arg_inputs is </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">)</span><span class="si">}</span><span class="s2"> but the length of arg_dynamic_shape is </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">)</span><span class="si">}</span><span class="s2">!&quot;</span>
                <span class="p">)</span>
                <span class="k">return</span> <span class="kc">False</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;kwarg_dynamic_shape is not provided!&quot;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;kwarg_inputs has </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2"> but kwarg_dynamic_shape has </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">! You may need to exclude keyword arguments with value None.&quot;</span>
                <span class="p">)</span>
                <span class="k">return</span> <span class="kc">False</span>

        <span class="k">return</span> <span class="kc">True</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_store_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">arg_inputs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="n">kwarg_inputs</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">_process_kwarg_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="c1"># Process kwarg inputs to be acceptable for Torch-TensorRT</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
            <span class="c1"># None should be excluded. AOT compile also does not allow dynamic control flow, bool is also excluded.</span>
            <span class="k">return</span> <span class="p">{</span>
                <span class="n">k</span><span class="p">:</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_process_kwarg_inputs</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
                <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
                <span class="k">if</span> <span class="p">(</span><span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span>
            <span class="p">}</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)):</span>
            <span class="k">return</span> <span class="n">inputs</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
            <span class="k">if</span> <span class="kc">None</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
                <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">inputs</span><span class="p">)(</span>
                    <span class="p">[</span>
                        <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_process_kwarg_inputs</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
                        <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span>
                    <span class="p">]</span>
                <span class="p">)</span>

        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Invalid input type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="si">}</span><span class="s2"> encountered in the input. &quot;</span>
            <span class="o">+</span> <span class="s2">&quot;Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}&quot;</span>
        <span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;Direct calls to </span><span class="si">{self.__class__}</span><span class="s2">.forward() are currently broken by due to https://github.com/pytorch/pytorch/issues/157183. Either call </span><span class="si">{self.__class__}</span><span class="s2">(...) directly or use </span><span class="si">{self.__class__}</span><span class="s2">._forward as a work around&quot;</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="c1"># Step 1: Check whether the input shape has changed</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_process_kwarg_inputs</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_validate_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

        <span class="c1"># Step 2: If the flag is unknown, it could be a recompile or refit.</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span> <span class="o">==</span> <span class="n">RefitFlag</span><span class="o">.</span><span class="n">UNKNOWN</span><span class="p">:</span>
            <span class="c1"># Update the flag</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">update_refit_condition</span><span class="p">()</span>

        <span class="c1"># Step 3: Refit/recompile accordingly</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span> <span class="o">==</span> <span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_RECOMPILE</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;(Re)Compiling the engine...&quot;</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">compile</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_store_state_dict_metadata</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">LIVE</span><span class="p">)</span>

        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span> <span class="o">==</span> <span class="n">RefitFlag</span><span class="o">.</span><span class="n">NEEDS_REFIT</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Model weight change detected. Refitting the module...&quot;</span><span class="p">)</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">refit_gm</span><span class="p">()</span>
            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Model refit failed. Recompiling the graph module.&quot;</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">compile</span><span class="p">()</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_store_state_dict_metadata</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">LIVE</span><span class="p">)</span>

        <span class="n">weight_streaming_ctx</span> <span class="o">=</span> <span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">weight_streaming_ctx</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">enable_weight_streaming</span> <span class="k">else</span> <span class="kc">None</span>
        <span class="p">)</span>
        <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gm</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="c1"># Storing inputs and outputs for verification when the state is unknown</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">run_info</span> <span class="o">=</span> <span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">result</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">result</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">to</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="s2">&quot;Trying to move the original PyTorch model. This will cause CPU offloading failing and increase GPU memory usage.&quot;</span>
            <span class="o">+</span> <span class="s2">&quot;If this is absolute necessary, please call module.pytorch_model.to(...) </span><span class="se">\n</span><span class="s2">&quot;</span>
            <span class="o">+</span> <span class="s2">&quot;The model is still on the original device.&quot;</span>
        <span class="p">)</span>

    <span class="nd">@property</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">to_torch_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trt_device</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">__deepcopy__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">memo</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="bp">cls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span>
        <span class="n">result</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
        <span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span> <span class="o">=</span> <span class="n">result</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="s2">&quot;pytorch_model&quot;</span><span class="p">:</span>
                <span class="nb">setattr</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">memo</span><span class="p">))</span>
        <span class="n">result</span><span class="o">.</span><span class="n">pytorch_model</span> <span class="o">=</span> <span class="n">_make_refit_change_trigger</span><span class="p">(</span>
            <span class="n">result</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">refit_state</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">result</span>

    <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="c1"># Due to https://github.com/pytorch/pytorch/issues/157183, we cannot use forward call, use _forward as a workaround.</span>
        <span class="c1"># This is a temporary fix.</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
            <span class="c1"># this object has it</span>
            <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>

        <span class="k">if</span> <span class="s2">&quot;pytorch_model&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
                <span class="s2">&quot;Module is not properly initiated. Pytorch model is not found in the module.&quot;</span>
            <span class="p">)</span>

        <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pytorch_model</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="fm">__delattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
            <span class="c1"># this object has it</span>
            <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__delattr__</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pytorch_model</span><span class="o">.</span><span class="fm">__delattr__</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># When the module finished initialization, any modification to attributes that does not exist</span>
        <span class="c1"># in __dict__ will be handled in pytorch module.</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_finished</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
                <span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="c1"># Capture attribute change</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">UNKNOWN</span><span class="p">)</span>
                <span class="c1"># We want to make sure the original PyTorch model does not have a trigger wrapper</span>
                <span class="n">value</span> <span class="o">=</span> <span class="n">recursively_remove_trigger</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
                <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">_check_inputs_shape</span><span class="p">(</span>
        <span class="n">input1</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
        <span class="n">input2</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
        <span class="n">dynamic_shapes</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">input1</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input2</span><span class="p">):</span>
                <span class="k">return</span> <span class="kc">False</span>
            <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">a</span><span class="p">),</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">input1</span><span class="p">),</span> <span class="n">input2</span><span class="p">):</span>
                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">type</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
                    <span class="k">return</span> <span class="kc">False</span>
                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> <span class="ow">and</span> <span class="n">a</span> <span class="o">!=</span> <span class="n">b</span><span class="p">:</span>
                    <span class="k">return</span> <span class="kc">False</span>
                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                    <span class="k">if</span> <span class="n">dynamic_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                            <span class="s2">&quot;Dynamic shape is not properly set but the input shape is changed!&quot;</span>
                        <span class="p">)</span>
                        <span class="k">return</span> <span class="kc">False</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">tensor_dynamic_shape</span> <span class="o">=</span> <span class="n">dynamic_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
                        <span class="k">if</span> <span class="ow">not</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_check_tensor_shapes_with_dynamic_shapes</span><span class="p">(</span>
                            <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">tensor_dynamic_shape</span>
                        <span class="p">):</span>
                            <span class="k">return</span> <span class="kc">False</span>

        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">input1</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="o">!=</span> <span class="n">input2</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
                <span class="k">return</span> <span class="kc">False</span>
            <span class="k">for</span> <span class="n">ka</span><span class="p">,</span> <span class="n">va</span> <span class="ow">in</span> <span class="n">input1</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="n">vb</span> <span class="o">=</span> <span class="n">input2</span><span class="p">[</span><span class="n">ka</span><span class="p">]</span>
                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">va</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">type</span><span class="p">(</span><span class="n">vb</span><span class="p">):</span>
                    <span class="k">return</span> <span class="kc">False</span>
                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">va</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> <span class="ow">and</span> <span class="n">va</span> <span class="o">!=</span> <span class="n">vb</span><span class="p">:</span>
                    <span class="k">return</span> <span class="kc">False</span>
                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">va</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="n">va</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">vb</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                    <span class="k">if</span> <span class="n">dynamic_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                            <span class="s2">&quot;Dynamic shape is not properly set but the input shape is changed!&quot;</span>
                        <span class="p">)</span>
                        <span class="k">return</span> <span class="kc">False</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">tensor_dynamic_shape</span> <span class="o">=</span> <span class="n">dynamic_shapes</span><span class="p">[</span><span class="n">ka</span><span class="p">]</span>
                        <span class="k">if</span> <span class="ow">not</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_check_tensor_shapes_with_dynamic_shapes</span><span class="p">(</span>
                            <span class="n">va</span><span class="p">,</span> <span class="n">vb</span><span class="p">,</span> <span class="n">tensor_dynamic_shape</span>
                        <span class="p">):</span>
                            <span class="k">return</span> <span class="kc">False</span>
                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span>
                    <span class="n">va</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span>
                <span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">MutableTorchTensorRTModule</span><span class="o">.</span><span class="n">_check_inputs_shape</span><span class="p">(</span>
                    <span class="n">va</span><span class="p">,</span> <span class="n">vb</span><span class="p">,</span> <span class="n">dynamic_shapes</span><span class="p">[</span><span class="n">ka</span><span class="p">]</span> <span class="k">if</span> <span class="n">dynamic_shapes</span> <span class="k">else</span> <span class="kc">None</span>
                <span class="p">):</span>
                    <span class="k">return</span> <span class="kc">False</span>
        <span class="k">return</span> <span class="kc">True</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">_check_tensor_shapes_with_dynamic_shapes</span><span class="p">(</span>
        <span class="n">input_1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">,</span> <span class="n">input_2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dynamic_shape</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">axis_0</span><span class="p">),</span> <span class="n">axis_1</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">input_1</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">input_2</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">axis_0</span> <span class="o">!=</span> <span class="n">axis_1</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dynamic_shape</span><span class="p">:</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                        <span class="s2">&quot;Dynamic shape does not include the axis on which input changes!&quot;</span>
                    <span class="p">)</span>
                    <span class="k">return</span> <span class="kc">False</span>
                <span class="n">dyn</span> <span class="o">=</span> <span class="n">dynamic_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
                <span class="k">if</span> <span class="n">axis_1</span> <span class="o">&gt;</span> <span class="n">dyn</span><span class="o">.</span><span class="n">max</span> <span class="ow">or</span> <span class="n">axis_1</span> <span class="o">&lt;</span> <span class="n">dyn</span><span class="o">.</span><span class="n">min</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="n">DynamicShapeOutOfRangeException</span><span class="p">(</span>
                        <span class="sa">f</span><span class="s2">&quot;Dimension (</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">) of new input tensor is not the range of supported shapes (saw: (</span><span class="si">{</span><span class="n">axis_1</span><span class="si">}</span><span class="s2">), expected: [</span><span class="si">{</span><span class="n">dyn</span><span class="o">.</span><span class="n">min</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">dyn</span><span class="o">.</span><span class="n">max</span><span class="si">}</span><span class="s2">])&quot;</span>
                    <span class="p">)</span>

        <span class="k">return</span> <span class="kc">True</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">serialize_dynamic_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">dims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">serializable_dynamic_shapes_dims</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">resursivly_serialize_dynamic_shape</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                <span class="k">for</span> <span class="n">axis</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obj</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">dynamic_shapes</span><span class="o">.</span><span class="n">_Dim</span><span class="p">):</span>
                        <span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
                        <span class="c1"># We use string of the hash to be the unique identifier of Dim object</span>
                        <span class="n">dims</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">hash</span><span class="p">(</span><span class="n">v</span><span class="p">)),</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">max</span><span class="p">))</span>
                        <span class="n">obj</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">hash</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">resursivly_serialize_dynamic_shape</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
                <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obj</span><span class="p">:</span>
                    <span class="n">resursivly_serialize_dynamic_shape</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>

        <span class="n">resursivly_serialize_dynamic_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">)</span>
        <span class="n">resursivly_serialize_dynamic_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">deserialize_dynamic_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">dims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">serializable_dynamic_shapes_dims</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">resursivly_deserialize_dynamic_shape</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                <span class="k">for</span> <span class="n">axis</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obj</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
                        <span class="n">obj</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">Dim</span><span class="p">(</span>
                            <span class="n">dims</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="nb">min</span><span class="o">=</span><span class="n">dims</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="nb">max</span><span class="o">=</span><span class="n">dims</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
                        <span class="p">)</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">resursivly_deserialize_dynamic_shape</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
                <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obj</span><span class="p">:</span>
                    <span class="n">resursivly_deserialize_dynamic_shape</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>

        <span class="n">resursivly_deserialize_dynamic_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_dynamic_shapes</span><span class="p">)</span>
        <span class="n">resursivly_deserialize_dynamic_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kwarg_dynamic_shapes</span><span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">save</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># Cast the object back to MutableTorchTensorRTModule to save</span>
        <span class="k">assert</span> <span class="p">(</span>
            <span class="ow">not</span> <span class="n">module</span><span class="o">.</span><span class="n">use_python_runtime</span>
        <span class="p">),</span> <span class="s2">&quot;Python runtime does not support serialization. Save failed.&quot;</span>
        <span class="n">module</span><span class="o">.</span><span class="n">init_finished</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="n">MutableTorchTensorRTModule</span>
        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">exp_program</span>
        <span class="n">module</span><span class="o">.</span><span class="n">pytorch_model</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">module</span><span class="o">.</span><span class="n">exp_program</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">module</span><span class="o">.</span><span class="n">serialize_dynamic_shapes</span><span class="p">()</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">pickle_protocol</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
        <span class="c1"># Restore deleted attributes</span>
        <span class="n">module</span><span class="o">.</span><span class="n">exp_program</span> <span class="o">=</span> <span class="n">exp_program</span>
        <span class="n">module</span><span class="o">.</span><span class="n">pytorch_model</span> <span class="o">=</span> <span class="n">_make_refit_change_trigger</span><span class="p">(</span>
            <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">refit_state</span>
        <span class="p">)</span>
        <span class="bp">cls</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span>
        <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
            <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
            <span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="vm">__class__</span><span class="p">),</span>
            <span class="p">{},</span>
        <span class="p">)</span>

        <span class="n">module</span><span class="o">.</span><span class="n">init_finished</span> <span class="o">=</span> <span class="kc">True</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="c1"># When the model get saved, init_finished is set to False.</span>
        <span class="c1"># Class is restored to MutableTorchTensorRTModule, and some attribute is deleted</span>
        <span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">weights_only</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">module</span><span class="o">.</span><span class="n">pytorch_model</span> <span class="o">=</span> <span class="n">_make_refit_change_trigger</span><span class="p">(</span>
            <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">refit_state</span>
        <span class="p">)</span>
        <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">to_torch_device</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
        <span class="n">module</span><span class="o">.</span><span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
            <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="n">module</span><span class="o">.</span><span class="n">kwarg_inputs</span>
        <span class="p">)</span>
        <span class="n">deallocate_module</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="p">,</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="bp">cls</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span>
        <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
            <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
            <span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">original_model</span><span class="o">.</span><span class="vm">__class__</span><span class="p">),</span>
            <span class="p">{},</span>
        <span class="p">)</span>
        <span class="n">module</span><span class="o">.</span><span class="n">deserialize_dynamic_shapes</span><span class="p">()</span>
        <span class="n">module</span><span class="o">.</span><span class="n">init_finished</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="k">return</span> <span class="n">module</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_reset_stateful_cache</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Does nothing. Support Huggingface CPU offload hooks. Override the huggingface cache reset function because we don&#39;t want the TRT module to be handled by HuggingFace.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span></div>


<span class="k">def</span><span class="w"> </span><span class="nf">recursively_remove_trigger</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
    <span class="c1"># Not safe: If the object has a circular reference (such as a doubly linkded list), this will cause infinite recursion</span>
    <span class="k">if</span> <span class="n">obj</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;ChangeTriggerWrapper&quot;</span><span class="p">:</span>
        <span class="n">obj</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">instance</span>

    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obj</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">obj</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">recursively_remove_trigger</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
            <span class="n">obj</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">recursively_remove_trigger</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="s2">&quot;__dict__&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="p">(</span><span class="nb">type</span><span class="p">,)):</span>
            <span class="k">return</span> <span class="n">obj</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obj</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="k">if</span> <span class="n">k</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;__&quot;</span> <span class="ow">or</span> <span class="n">k</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span> <span class="o">!=</span> <span class="s2">&quot;__&quot;</span><span class="p">:</span>
                <span class="c1"># We don&#39;t want to touch some built in attribute such as __dict__</span>
                <span class="nb">setattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">recursively_remove_trigger</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>

    <span class="k">return</span> <span class="n">obj</span>


<span class="k">def</span><span class="w"> </span><span class="nf">_make_refit_change_trigger</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">refit_state</span><span class="p">:</span> <span class="n">RefitState</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
    <span class="n">subclass</span><span class="p">:</span> <span class="nb">type</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="vm">__class__</span>

    <span class="k">class</span><span class="w"> </span><span class="nc">ChangeTriggerWrapper</span><span class="p">(</span><span class="n">subclass</span><span class="p">):</span>  <span class="c1"># type: ignore</span>
        <span class="c1"># The reason why we want to inherent to the subclass is that we want the ChangeTriggerWrapper shares all functions</span>
        <span class="c1"># that an ordinary object has. In this way attributes accessed inside a function will be from the __getattr__function</span>
        <span class="c1"># of ChangeTriggerWrapper, instead of the object itself, thus be recursively wrapped by ChangeTriggerWrapper.</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
            <span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;instance&quot;</span><span class="p">,</span> <span class="n">obj</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</span><span class="p">(</span>
            <span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span>
        <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>  <span class="c1"># Called when the attribute does not exist</span>
            <span class="n">obj</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">):</span>
                <span class="c1"># Whenever the user retrieve an attribute that could be related to weights, we set the state to UNKNOWN</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_on_change</span><span class="p">()</span>
            <span class="k">if</span> <span class="p">(</span>
                <span class="nb">hasattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="s2">&quot;__dict__&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span>
            <span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span>
                <span class="n">obj</span><span class="p">,</span> <span class="n">ChangeTriggerWrapper</span>
            <span class="p">):</span>  <span class="c1"># prevent nesting wrapper</span>
                <span class="k">return</span> <span class="n">_make_refit_change_trigger</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">refit_state</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">obj</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
            <span class="c1"># If we need to set __dict__ or instance, we directly set it to the trigger wrapper.</span>
            <span class="c1"># Enable setting __dict__ is because PyTorch proxy uses __new__ to initialize a shallow copy</span>
            <span class="c1"># of a module and explicit set the __dict__. If we don&#39;t set __dict__ it will get infinite recursion.</span>
            <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;__dict__&quot;</span><span class="p">,</span> <span class="s2">&quot;instance&quot;</span><span class="p">]:</span>
                <span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
                <span class="k">return</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_on_change</span><span class="p">()</span>
            <span class="c1"># We want to make sure the original PyTorch model does not have a trigger wrapper</span>
            <span class="n">value</span> <span class="o">=</span> <span class="n">recursively_remove_trigger</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
            <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__delattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_on_change</span><span class="p">()</span>
            <span class="nb">delattr</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="p">,</span>
                <span class="n">name</span><span class="p">,</span>
            <span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">_on_change</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">refit_state</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">RefitFlag</span><span class="o">.</span><span class="n">UNKNOWN</span><span class="p">)</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
                <span class="s2">&quot;Attribute modification detected. The module will be refitted later.&quot;</span>
            <span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">_call_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__setitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_on_change</span><span class="p">()</span>
            <span class="c1"># We want to make sure the original PyTorch model does not have a trigger wrapper</span>
            <span class="n">value</span> <span class="o">=</span> <span class="n">recursively_remove_trigger</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="o">.</span><span class="fm">__setitem__</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">items</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
            <span class="n">obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">ChangeTriggerWrapper</span><span class="p">):</span>
                <span class="k">return</span> <span class="n">obj</span>
            <span class="k">return</span> <span class="n">_make_refit_change_trigger</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">refit_state</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
            <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterator</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
            <span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instance</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">ChangeTriggerWrapper</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
</pre></div>

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